Causal and Fair Machine Learning

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Project Description

The exact topic depends on the student's interest, student's background, and previous research experience. Generally speaking, there are mainly three topics of this project: 1. Designing Fair Machine Learning Algorithms. In this project, students will focus on how to make the current machine learning algorithms be fair. They will also explore the fairness issue of the current machine learning algorithms, especially for healthcare data. 2. Causality as a tool for de-biasing current deep learning algorithms. Students will use the idea of causality to different deep learning tasks to de-bias the datasets or algorithms in order to improve the accuracy and trustworthiness. 3. Causality as a tool for invariant learning. This project mainly focuses on transfer learning, students will use causality to design transfer learning algorithms.
Program - Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Field of Study - Causal Inference, Fairness, Transfer Learning, Deep Learning

About the
Researcher

Di Wang

Di Wang

Desired Project Deliverables

During the project, students will have opportunity to learn about some topics in trustworthy machine learning, especially fair learning, transfer learning and causal learning. They will learn and implement the SOTA methods. Hopefully, they may produce some publication after the intern.

RECOMMENDED STUDENT ACADEMIC & RESEARCH BACKGROUND

Computer Science
Computer Science
Applied Mathematics
Applied Mathematics
Statistics
Statistics
Deep Learning
Deep Learning